
where \(\circledast \)  is the convolutional operation, \(\mathbf {O}_l\in \mathcal {R}^{N\times C_{in}^{l+1}\times H_{l+1} \times W_{l+1}}\) , and \(C_{in}^{l+1}\) =\(C_{out}^l\) .
As a first step, the client will record the feature map generated by local model in each layer.
Then, we apply ReLU [1]} followed by an average pooling operation over the feature map.
For the channels at \(l\) -th layer, the activation of output feature map is denoted by \(\mathbf {A}_l\) , can be calculated by
\(\mathbf {A}_l=\text{AvgPooling}(\text{ReLU}(\mathbf {O}_l))\) 
